Multisite Weather Generators Using Bayesian Networks: An Illustrative Case Study for Precipitation Occurrence

ABSTRACT: Many existing approaches for multisite weather generation try to capture several statistics of the observed data (e.g. pairwise correlations) in order to generate spatially and temporarily consistent series. In this work we analyse the application of Bayesian networks to this problem, focusing on precipitation occurrence and considering a simple case study to illustrate the potential of this new approach. We use Bayesian networks to approximate the multi-variate (-site) probability distribution of observed gauge data, which is factorized according to the relevant (marginal and conditional) dependencies. This factorization allows the simulation of synthetic samples from the multivariate distribution, thus providing a sound and promising methodology for multisite precipitation series generation.We acknowledge funding provided by the project MULTI‐SDM (CGL2015‐ 66583‐R, MINECO/FEDER)

The problem of scale in predicting biological responses to climate

This is the final version. Available on open access from Wiley via the DOI in this record Many analyses of biological responses to climate rely on gridded climate data derived from weather stations, which differ from the conditions experienced by organisms in at least two respects. First, the microclimate recorded by a weather station is often quite different to that near the ground surface, where many organisms live. Second, the temporal and spatial resolutions of gridded climate datasets derived from weather stations are often too coarse to capture the conditions experienced by organisms. Temporally and spatially coarse data have clear benefits in terms of reduced model size and complexity, but here we argue that coarse-grained data introduce errors that, in biological studies, are too often ignored. However, in contrast to common perception, these errors are not necessarily caused directly by a spatial mismatch between the size of organisms and the scale at which climate data are collected. Rather, errors and biases are primarily due to (i) systematic discrepancies between the climate used in analysis and that experienced by organisms under study and (ii) the non-linearity of most biological responses in combination with differences in climate variance between locations and time periods for which models are fitted and those for which projections are made. We discuss when exactly problems of scale can be expected to arise and highlight the potential to circumvent these by spatially and temporally down-scaling climate. We also suggest ways in which adjustments to deal with issues of scale could be made without the need to run high-resolution models over wide extents.European Regional Development Fund (ERDF

Statistical modeling of the space–time relation between wind and significant wave height

Many marine activities, such as designing ocean structures and planning marine operations, require the characterization of sea-state climate. This study investigates the statistical relationship between wind and sea states, considering its spatiotemporal behavior. A transfer function is established between wind fields over the North Atlantic (predictors) and the significant wave height (predictand) at three locations: southwest of the French coast (Gironde), the English Channel, and the Gulf of Maine. The developed method considers both wind seas and swells by including local and global predictors. Using a fully data-driven approach, the global predictors' spatiotemporal structure is defined to account for the non-local and non-instantaneous relationship between wind and waves. Weather types are constructed using a regression-guided clustering method, and the resulting clusters correspond to different wave systems (swells and wind seas). Then, in each weather type, a penalized linear regression model is fitted between the predictor and the predictand. The validation analysis proves the models skill in predicting the significant wave height, with a root mean square error of approximately 0.3 m in the three considered locations. Additionally, the study discusses the physical insights underlying the proposed method.</p

Long Term Object Drift Forecast in the Ocean With Tide and

In this paper, we propose a new method to forecast the drift of objects in near coastal ocean on a period of several weeks. The proposed approach consists in estimating the probability of events linked to the drift using Monte Carlo simulations. It couples an averaging method which permits to decrease the computational cost and a statistical method in order to take into account the variability of meteorological loading factors